The more complex problems to be solved become, the higher rises the chance that the problem may be solvable using a large collection of elements instrumental in solving the related task.
Examples may be an artificial neural network with a plurality of neuronal and synaptic elements. One of such synaptic element could be represented by a collection of synaptic sub-elements. Other examples may be an information storage system with a large plurality of storage elements, a large plurality of sensors and actuators in an IoT (Internet-of-Things or Industry 4.0) environment, or a collection of robots collectively performing a certain task. In case of an information storage system, each storage element could comprise multiple storage sub-elements.
The underlying problem is very often a common problem: how to synchronize the different activities of the plurality of elements. Sometimes, the elements are divided into sub-elements. In this case, a proper synchronization becomes even harder. In other words, in the above-mentioned examples, it may be essential to develop an arbitration scheme by which each individual sub-element is selected and enabled to contribute to the overall functionality of the element in the network.
There are several disclosures related to a method for achieving a collective task. Document US 20160055409 A1 proposes a method of selecting/classifying individual sub-elements (objects) for a large number of elements (objects) for a system like a storage system sensor network, neuronal network, etc. The method also discloses applying multiple confidence values for multiple objects.
Document US 20150019468 A1 discloses a method of grouping sub-elements for a large number of elements for a system including a modification of adaptive synaptic weights according to anti-hebbian and hebbian plasticity, wherein the adaptive synaptic weights are configured in form of a differential pair of memristors.
One disadvantage of known technology is a missing synchronization of the sub-elements in order to optimize the functioning of the network system. One approach may be to operate all of the sub-elements in parallel or use an aggregate for the main behavior. There are some approaches where the sub-elements are selected randomly with the use of an in-built pseudorandom number generator for elements. Both of these approaches increase the level of complexity and unpredictability.
Thus, there may be a need to overcome the limitations of the known technology and provide technology allowing a proper synchronization and arbitration of sub-elements in order to enable a system of elements achieving a common task.